Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling
Yutai Hou, Yongkui Lai, Cheng Chen, Wanxiang Che, Ting Liu · 2021
In this paper, we investigate few-shot joint learning for dialogue language understanding.Most existing few-shot models learn a single task each time with only a few examples.However, dialogue language understanding contains two closely related tasks, i.e., intent detection and slot filling, and often benefits from jointly learning the two tasks.This calls for new few-shot learning techniques that are able to capture task relations from only a few examples and jointly learn multiple tasks.To achieve this, we propose a similarity-based few-shot learning scheme, named Contrastive Prototype Merging network (ConProm), that learns to bridge metric spaces of intent and slot on data-rich domains, and then adapt the bridged metric space to specific few-shot domain.Experiments on two public datasets, Snips and FewJoint, show that our model significantly outperforms the strong baselines in one and five shots settings.